Abstract In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50% outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50% of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Novel Robust Statistical Method for Background Initialization and Visual Surveillance


    Beteiligte:
    Wang, Hanzi (Autor:in) / Suter, David (Autor:in)


    Erscheinungsdatum :

    2006-01-01


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A Novel Robust Statistical Method for Background Initialization and Visual Surveillance

    Wang, H. / Suter, D. | British Library Conference Proceedings | 2006


    A background model initialization algorithm for video surveillance

    Gutchess, D. / Trajkovics, M. / Cohen-Solal, E. et al. | IEEE | 2001


    A Background Model Initialization Algorithm for Video Surveillance

    Gutchess, D. / Trajkovic, M. / Cohen-Solal, E. et al. | British Library Conference Proceedings | 2001


    A Statistical Approach to Background Subtraction for Surveillance Systems

    Ohta, N. / IEEE | British Library Conference Proceedings | 2001